# spacepy.pycdf.CDF

class `spacepy.pycdf.``CDF`(pathname, masterpath=None, create=None, readonly=None)

  Python object representing a CDF file.

  Open or create a CDF file by creating an object of this class.

  Parameters: | pathname : string name of the file to open or create masterpath : string name of the master CDF file to use in creating a new file. If not provided, an existing file is opened; if provided but evaluates to `False` (e.g., `''`), an empty new CDF is created. create : bool Create a new CDF even if masterpath isn’t provided readonly : bool Open the CDF read-only. Default True if opening an existing CDF; False if creating a new one. A readonly CDF with many variables may be slow to close. See `readonly()`.
  Raises: | CDFError if CDF library reports an error
  Warns: | CDFWarning if CDF library reports a warning and interpreter is set to error on warnings.

  Examples

  Open a CDF by creating a CDF object, e.g.:

      >>> cdffile = pycdf.CDF('cdf_filename.cdf')

  Be sure to `close()` or `save()` when done.

  Note

  Existing CDF files are opened read-only by default, see `readonly()` to change.

  CDF supports the with keyword, like other file objects, so:

      >>> with pycdf.CDF('cdf_filename.cdf') as cdffile:
      ...     #do brilliant things with the CDF

  will open the CDF, execute the indented statements, and close the CDF when finished or when an error occurs. The python docs include more detail on this ‘context manager’ ability.

  CDF objects behave like a python dictionary, where the keys are names of variables in the CDF, and the values, `Var` objects. As a dictionary, they are also iterable and it is easy to loop over all of the variables in a file. Some examples:

  > 1. List the names of all variables in the open CDF `cdffile`:

  >        >>> cdffile.keys()
  >        >>> for k in cdffile: #Alternate
  >        ...     print(k)

  > 2. Get a `Var` object for the variable named `Epoch`:

  >        >>> epoch = cdffile['Epoch']

  > 3. Determine if a CDF contains a variable named `B_GSE`:

  >        >>> if 'B_GSE' in cdffile:
  >        ...     print('B_GSE is in the file')
  >        ... else:
  >        ...     print('B_GSE is not in the file')

  > 4. Find how many variables are in the file:

  >        >>> print(len(cdffile))

  > 5. Delete the variable `Epoch` from the open CDF file `cdffile`:

  >        >>> del cdffile['Epoch']

  > 6. Display a summary of variables and types in open CDF file `cdffile`:

  >        >>> print(cdffile)

  > 7. Open the CDF named `cdf_filename.cdf`, read all the data from all variables into dictionary `data`, and close it when done or if an error occurs:

  >        >>> with pycdf.CDF('cdf_filename.cdf') as cdffile:
  >        ...     data = cdffile.copy()

  This last example can be very inefficient as it reads the entire CDF. Normally it’s better to treat the CDF as a dictionary and access only the data needed, which will be pulled transparently from disc. See `Var` for more subtle examples.

  Potentially useful dictionary methods and related functions:

  > - in
  > - keys
  > - `len()`
  > - list comprehensions
  > - `sorted()`
  > - `dictree()`

  The CDF user’s guide section 2.2 has more background information on CDF files.

  The `attrs` Python attribute acts as a dictionary referencing CDF attributes (do not confuse the two); all the dictionary methods above also work on the attribute dictionary. See `gAttrList` for more on the dictionary of global attributes.

  Creating a new CDF from a master (skeleton) CDF has similar syntax to opening one:

      >>> cdffile = pycdf.CDF('cdf_filename.cdf', 'master_cdf_filename.cdf')

  This creates and opens `cdf_filename.cdf` as a copy of `master_cdf_filename.cdf`.

  Using a skeleton CDF is recommended over making a CDF entirely from scratch, but this is possible by specifying a blank master:

      >>> cdffile = pycdf.CDF('cdf_filename.cdf', '')

  When CDFs are created in this way, they are opened read-write, see `readonly()` to change.

  By default, new CDFs (without a master) are created in version 2 (backward-compatible) format. To create a version 3 CDF, use `Library.set_backward()`:

      >>> pycdf.lib.set_backward(False)
      >>> cdffile = pycdf.CDF('cdf_filename.cdf', '')

  Add variables by direct assignment, which will automatically set type and dimension based on the data provided:

      >>> cdffile['new_variable_name'] = [1, 2, 3, 4]

  or, if more control is needed over the type and dimensions, use `new()`.

  Although it is supported to assign Var objects to Python variables for convenience, there are some minor pitfalls that can arise when changing a CDF that will not affect most users. This is only a concern when assigning a zVar object to a Python variable, changing the CDF through some other variable, and then trying to use the zVar object via the originally assigned variable.

  Deleting a variable:

      >>> var = cdffile['Var1']
      >>> del cdffile['Var1']
      >>> var[0] #fail, no such variable

  Renaming a variable:

      >>> var = cdffile['Var1']
      >>> cdffile['Var1'].rename('Var2')
      >>> var[0] #fail, no such variable

  Renaming via the same variable works:

      >>> var = cdffile['Var1']
      >>> var.rename('Var2')
      >>> var[0] #succeeds, aware of new name

  Deleting a variable and then creating another variable with the same name may lead to some surprises:

      >>> var = cdffile['Var1']
      >>> var[...] = [1, 2, 3, 4]
      >>> del cdffile['Var1']
      >>> cdffile.new('Var1', data=[5, 6, 7, 8]
      >>> var[...]
      [5, 6, 7, 8]

  `attr_num`(attrname) | Get the attribute number and scope by attribute name
  `attrs` | Global attributes for this CDF in a dict-like format.
  `add_attr_to_cache`(attrname, num, scope) | Add an attribute to the name-to-number cache
  `add_to_cache`(varname, num) | Add a variable to the name-to-number cache
  `CDF.backward` |
  `checksum`([new_val]) | Set or check the checksum status of this CDF.
  `clear_attr_from_cache`(attrname) | Mark an attribute deleted in the name-to-number cache
  `clear_from_cache`(varname) | Mark a variable deleted in the name-to-number cache
  `clone`(zVar[, name, data]) | Clone a zVariable (from another CDF or this) into this CDF
  `close`() | Closes the CDF file
  `col_major`([new_col]) | Finds the majority of this CDF file
  `compress`([comptype, param]) | Set or check the compression of this CDF
  `copy`() | Make a copy of all data and attributes in this CDF
  `from_data`(filename, sd) | Create a new CDF file from a SpaceData object or similar
  `new`(name[, data, type, recVary, dimVarys, ...]) | Create a new zVariable in this CDF
  `raw_var`(name) | Get a “raw” `Var` object.
  `readonly`([ro]) | Sets or check the readonly status of this CDF
  `save`() | Saves the CDF file but leaves it open.
  `var_num`(varname) | Get the variable number of a particular variable name
  `version`() | Get version of library that created this CDF

  `attrs`

    Global attributes for this CDF in a dict-like format. See `gAttrList` for details.

  `backward`

    True if this CDF was created in backward-compatible mode (for opening with CDF library before 3.x)

  `add_to_cache`(varname, num)

    Add a variable to the name-to-number cache

    This maintains a cache of name-to-number mappings for zVariables to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | varname : bytes name of the zVariable. Not this is NOT a string in Python 3! num : int number of the variable

  `add_attr_to_cache`(attrname, num, scope)

    Add an attribute to the name-to-number cache

    This maintains a cache of name-to-number mappings for attributes to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | varname : bytes name of the zVariable. Not this is NOT a string in Python 3! num : int number of the variable scope : bool True if global scope; False if variable scope.

  `attr_num`(attrname)

    Get the attribute number and scope by attribute name

    This maintains a cache of name-to-number mappings for attributes to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | attrname : bytes name of the zVariable. Not this is NOT a string in Python 3!
    Returns: | out : tuple attribute number, scope (True for global) of this attribute
    Raises: | CDFError : if variable is not found

  `checksum`(new_val=None)

    Set or check the checksum status of this CDF. If checksums are enabled, the checksum will be verified every time the file is opened.

    Returns: | out : boolean True if the checksum is enabled or False if disabled
    Other Parameters:
    | new_val : boolean True to enable checksum, False to disable, or leave out to simply check.

  `clear_from_cache`(varname)

    Mark a variable deleted in the name-to-number cache

    Will remove a variable, and all variables with higher numbers, from the variable cache.

    Does NOT delete the variable!

    This maintains a cache of name-to-number mappings for zVariables to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | varname : bytes name of the zVariable. Not this is NOT a string in Python 3!

  `clear_attr_from_cache`(attrname)

    Mark an attribute deleted in the name-to-number cache

    Will remove an attribute, and all attributes with higher numbers, from the attribute cache.

    Does NOT delete the variable!

    This maintains a cache of name-to-number mappings for attributes to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | attrname : bytes name of the attribute. Not this is NOT a string in Python 3!

  `clone`(zVar, name=None, data=True)

    Clone a zVariable (from another CDF or this) into this CDF

    Parameters: | zVar : `Var` variable to clone
    Returns: | out : `Var` The newly-created zVar in this CDF
    Other Parameters:
    | name : str Name of the new variable (default: name of the original) data : boolean (optional) Copy data, or only type, dimensions, variance, attributes? (default: True, copy data as well)

  `close`()

    Closes the CDF file

    Although called on object destruction (`__del__()`), to ensure all data are saved, the user should explicitly call `close()` or `save()`.

    Raises: | CDFError : if CDF library reports an error
    Warns: | CDFWarning : if CDF library reports a warning

  `col_major`(new_col=None)

    Finds the majority of this CDF file

    Returns: | out : boolean True if column-major, false if row-major
    Other Parameters:
    | new_col : boolean Specify True to change to column-major, False to change to row major, or do not specify to check the majority rather than changing it. (default is check only)

  `compress`(comptype=None, param=None)

    Set or check the compression of this CDF

    Sets compression on entire file, not per-variable.

    See section 2.6 of the CDF user’s guide for more information on compression.

    Returns: | out : tuple (comptype, param) currently in effect
    Other Parameters:
    | comptype : ctypes.c_long type of compression to change to, see CDF C reference manual section 4.10. Constants for this parameter are in `const`. If not specified, will not change compression. param : ctypes.c_long Compression parameter, see CDF CRM 4.10 and `const`. If not specified, will choose reasonable default (5 for gzip; other types have only one possible parameter.)

    See also

    `Var.compress()`

    Examples

    Set file `cdffile` to gzip compression, compression level 9:

          >>> cdffile.compress(pycdf.const.GZIP_COMPRESSION, 9)

  `copy`()

    Make a copy of all data and attributes in this CDF

    Returns: | out : `CDFCopy` `SpaceData`-like object of all data

  classmethod `from_data`(filename, sd)

    Create a new CDF file from a SpaceData object or similar

    The CDF named `filename` is created, opened, filled with the contents of `sd` (including attributes), and closed.

    `sd` should be a dictionary-like object; each key will be made into a variable name. An attribute called `attrs`, if it exists, will be made into global attributes for the CDF.

    Each value of `sd` should be array-like and will be used as the contents of the variable; an attribute called `attrs`, if it exists, will be made into attributes for that variable.

    Parameters: | filename : string name of the file to create sd : spacepy.datamodel.SpaceData data to put in the CDF. This structure cannot be nested, i.e., it must contain only `dmarray` and no `Spacedata` objects.

  `new`(name, data=None, type=None, recVary=True, dimVarys=None, dims=None, n_elements=None, compress=None, compress_param=None)

    Create a new zVariable in this CDF

    Note

    Either `data` or `type` must be specified. If type is not specified, it is guessed from `data`.

    Parameters: | name : str name of the new variable
    Returns: | out : `Var` the newly-created zVariable
    Other Parameters:
    | data data to store in the new variable. If this has a an `attrs` attribute (e.g., `dmarray`), it will be used to populate attributes of the new variable. type : ctypes.c_long CDF type of the variable, from `const`. See section 2.5 of the CDF user’s guide for more information on CDF data types. recVary : boolean record variance of the variable (default True) dimVarys : list of boolean dimension variance of each dimension, default True for all dimensions. dims : list of int size of each dimension of this variable, default zero-dimensional. Note this is the dimensionality as defined by CDF, i.e., for record-varying variables it excludes the leading record dimension. See `Var`. n_elements : int number of elements, should be 1 except for CDF_CHAR, for which it’s the length of the string. compress : ctypes.c_long Compression to apply to this variable, default None. See `Var.compress()`. compress_param : ctypes.c_long Compression parameter if compression used; reasonable default is chosen. See `Var.compress()`.
    Raises: | ValueError : if neither data nor sufficient typing information is provided.

    Notes

    Any given data may be representable by a range of CDF types; if the type is not specified, pycdf will guess which the CDF types which can represent this data. This breaks down to:

    > 1. If input data is a numpy array, match the type of that array
    > 2. Proper kind (numerical, string, time)
    > 3. Proper range (stores highest and lowest number provided)
    > 4. Sufficient resolution (EPOCH16 required if datetime has microseconds or below.)

    If more than one value satisfies the requirements, types are returned in preferred order:

    > 1. Type that matches precision of data first, then
    > 2. integer type before float type, then
    > 3. Smallest type first, then
    > 4. signed type first, then
    > 5. specifically-named (CDF_BYTE) vs. generically named (CDF_INT1)

    So for example, EPOCH_16 is preferred over EPOCH if `data` specifies below the millisecond level (rule 1), but otherwise EPOCH is preferred (rule 2).

    For floats, four-byte is preferred unless eight-byte is required:

    > 1. absolute values between 0 and 3e-39
    > 2. absolute values greater than 1.7e38

    This will switch to an eight-byte double in some cases where four bytes would be sufficient for IEEE 754 encoding, but where DEC formats would require eight.

  `raw_var`(name)

    Get a “raw” `Var` object.

    Normally a `Var` will perform translation of values for certain types (to/from Unicode for CHAR variables on Py3k, and to/from datetime for all time types). A “raw” object does not perform this translation, on read or write.

    This does not affect the data on disk, and in fact it is possible to maintain multiple Python objects with access to the same zVariable.

    Parameters: | name : str name or number of the zVariable

  `readonly`(ro=None)

    Sets or check the readonly status of this CDF

    If the CDF has been changed since opening, setting readonly mode will have no effect.

    Note

    Closing a CDF that has been opened readonly, or setting readonly False, may take a substantial amount of time if there are many variables in the CDF, as a (potentially large) cache needs to be cleared. Consider specifying `readonly=False` when opening the file if this is an issue. However, this may make some reading operations slower.

    Returns: | out : Boolean True if CDF is read-only, else False
    Other Parameters:
    | ro : Boolean True to set the CDF readonly, False to set it read/write, or leave out to check only.
    Raises: | CDFError : if bad mode is set

  `save`()

    Saves the CDF file but leaves it open.

    If closing the CDF, `close()` is sufficient; there is no need to call `save()` before `close()`.

    Note

    Relies on an undocumented call of the CDF C library, which is also used in the Java interface.

    Raises: | CDFError : if CDF library reports an error
    Warns: | CDFWarning : if CDF library reports a warning

  `var_num`(varname)

    Get the variable number of a particular variable name

    This maintains a cache of name-to-number mappings for zVariables to keep from having to query the CDF library constantly. It’s mostly an internal function.

    Parameters: | varname : bytes name of the zVariable. Not this is NOT a string in Python 3!
    Returns: | out : int Variable number of this zvariable.
    Raises: | CDFError : if variable is not found

  `version`()

    Get version of library that created this CDF

    Returns: | out : tuple version of CDF library, in form (version, release, increment)